A kernel- and optimal transport- based test of independence between covariates and right-censored lifetimes
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Publication:6320224
arXiv1906.03866MaRDI QIDQ6320224
David Rindt, David Steinsaltz, Dino Sejdinovic
Publication date: 10 June 2019
Abstract: We propose a nonparametric test of independence, termed optHSIC, between a covariate and a right-censored lifetime. Because the presence of censoring creates a challenge in applying the standard permutation-based testing approaches, we use optimal transport to transform the censored dataset into an uncensored one, while preserving the relevant dependencies. We then apply a permutation test using the kernel-based dependence measure as a statistic to the transformed dataset. The type 1 error is proven to be correct in the case where censoring is independent of the covariate. Experiments indicate that optHSIC has power against a much wider class of alternatives than Cox proportional hazards regression and that it has the correct type 1 control even in the challenging cases where censoring strongly depends on the covariate.
Has companion code repository: https://github.com/davidrindt/kernel_logrank_python_code
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